Impulsive sounds are defined as short duration, high energy sounds usually caused by an impact. Examples of impulsive sounds include gear rattle, body squeaks and rattles, strut chuckle, ABS, driveline backlash, ticking from valve-train and fuel injectors, impact harshness, and engine rattles. Methods that can determine and predict the audible threshold of these impulse sounds, as well as identify their above-threshold characteristics, are important tools. The ability to predict thresholds is useful for cascading vehicle-level thresholds down to component-level thresholds, and ultimately, in developing appropriate bench tests for system components. Identifying the above-threshold characteristics is useful as a diagnostic tool for identifying impulsive sounds in a vehicle, and also for developing relevant sound quality methods.
Three properties of a detection and classification algorithm are desired. The first is to detect different classes of impulsive sounds without having to subjectively tune algorithm parameters for each class. The second is to identify the temporal and spectral characteristics of the impulsive sounds. The final desired property is to correlate predicted thresholds with subjective detection thresholds. Existing algorithms do not satisfy all three properties. Current algorithms that identify temporal and spectral characteristics typically require subjective tuning of parameters for each class in order to correlate with subjective thresholds. Further, algorithms that automatically identify impulses in a sound do not characterize both the temporal and spectral content of the impulses.
Correlation to subjective thresholds is largely due to processing the sound with a model of the auditory system. This provides the temporal and spectral data relevant to hearing. Most algorithms use wavelets or other time-frequency techniques, and as a result, it is difficult to generalize hearing properties to these models. Of the current algorithms that are based on auditory models, they require subjective interpretation of the temporal and spectral information to identify the impulsive sounds.
It is therefore desired to have a method and implementation for detecting and characterizing audible transients in noise, specifically having automated interpretation of temporal and spectral information, and the ability to identify impulsive sounds over a large range of background sound levels.